16 ways to generate value from churn models

Unlike Gen AI, churn models have been around for quite a while. Probably since the mid 1990s. In the fauna of the data science world they are comparable to dinosaurs at this point.

Given their relatively long history in the data and analytics space, you might think we had exhausted all the possible value from these models and that there weren’t any opportunities left? Wrong. Traditional machine learning methods — like churn modelling — still have plenty of gas left, and most companies are not near to utilizing them to their full potential. These models have the potential to improve businesses for years to come.

Below I have collected a selection of 16 different ways companies can utilize churn models to create value. The topics are diverse and range from retention campaigns to financial planning, but the ability to understand when a customer is likely to leave remains the central theme.

1. Contract Renewal

• Use Case: Identify customers close to the end of their contract who are likely to switch to competitors
• Example: A B2B IT service provider creates special renewal offers for at-risk clients.
• Example 2: A magazine with a subscription services contacts customers who are predicted to churn and offers them early renewal benefits.

2. Product Pricing & Price Elasticity

• Use Case: Optimize pricing strategies and products to match customer price elasticity (model churn as a function of price)
• Example: An energy company uses a churn model with price as an explanatory variable to optimize their product pricing in line with their strategic objectives.

3. Loyalty Card Program

• Use Case: Use promotions, discounts and special bonus points with loyalty programs to motivate customers at risk of churn.
• Example: An airline rewards bonus loyalty points to customers who have not flown in a while.

4. Financial Planning

• Use Case: Forecast future revenue based on expected churn rates.
• Example: A utility uses the churn rates to accurately forecast the next years revenue from a cohort of customers.

5. Scenario Building

• Use Case: Companies can predict how their future customer base will evolve under various future scenarios and develop robust strategies to deal with the expected outcomes.
• Example: An energy company wants to understand how its customer base will develop depending on the future market price of electricity and uses a churn function that has the price of power as an explanatory variable to estimate multiple different scenarios.

6. Promotional Campaigns

• Use Case: Use a churn model to identify customers at high risk for churn and target them with specific offers and promotions
• Example: An online retailer creates a targeted promotional campaign towards high-risk churn customers

7. Optimal Sourcing

• Use Case: By using a combination of short-term and long-term churn forecasting, companies can adapt more flexible sourcing strategies for products that need to be delivered to their customers.
• Example: An energy company switches to short-term sourcing of back-end energy contracts and while using 1-month churn models to predict demand for energy, this lets them exploit the flexibility of short-term sourcing to increase expected profits on fixed-term contracts.

8. Workforce planning

• Use Case: Churn predictions help inform staffing requirements for sales and customer support roles.
• Example: A B2B SaaS company can adjust its call center workforce based on predicted number of customers that haven’t churned.

9. Segmented Product Creation

• Use Case: Product creation based on customer segments at differing risk of churn, optimizing revenues and retention within each segment.
• Example: A teclo company segments customers based on churn risk and creates different products to cater to the different categories of customers.

10. Customer Life Time Value (CLV) Estimation

• Use Case: Combine churn predictions with customer value data to focus retention efforts on high-value customers.
• Example: A P&C insurance company uses the churn risk together with the risk of accident to predict the value of individual customers and of the total customer portfolio.

11. Targeted Discounting

• Use Case: Offer targeted discounts to customers at high risk of churning
• Example: At risk customers at a telco are offered specific discounts to mitigate the churn risk.
• Example 2: A subscription-based video streaming service offers a temporary price reduction to retain customers.

12. Lead Generation

• Use Case: Build churn models on existing customers and score prospects, identity the best potential new customers with low churn risk.
• Example: A financial services group uses the churn risk of its banking customers to identify possible leads for their insurance products.

13. Company Value Estimation

• Use Case: Use churn rates to predict future cash flow.
• Example: An PE company is considering the takeover of gym chain. It uses the churn risk of the existing customer base together with the acquisition rate and expected monthly membership fee to determine the future discounted cash flow of the gym.

14. Upselling and Cross-Selling

• Use Case: Use churn predictions to identify opportunities for cross-selling or upselling complementary products or services.
• Example: An online retailer offers related products to customers who haven’t purchased recently.

15. Product or Service Improvements

• Use Case: Use churn risk along with customer surveys to identify pain points or dissatisfaction areas
• Example: A B2B SaaS company creates a better onboarding process base on feedback from high churn risk customers.

16. Proactive Support Outreach

• Use Case: Flag customers for personalized support based on their churn risk.
• Example: A B2C software company reaches out to users who encountered frequent issues and have an increased churn rate.

Having reviewed the list above, do you honestly think your company is using churn models to their full advantage? Before planning that next Gen AI project, consider getting value from some proven machine learning models first.

Did I miss anything? Is your company using churn models in a different or unique way? I would love to hear about it! Let me know in the comments!

Special Note to European Readers

While most of the use cases below are fine to use, some of them might intersect with current GDPR regulation. Especially for B2C, some of the more targeted approaches can be hard to implement without jumping through a lot of hoops related to consent etc. B2B customers are generally expected to professionals so the regulation is more relaxed for them.

Check with your legal team if you are unsure, or get in touch if you would like to discuss it. I’ve advised multiple companies on how to stay within the bounds of GDPR while still retaining value from their churn and customer base management models.